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Value-at-risk optimal policies for revenue management problems

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Value-at-risk optimal policies for revenue management problems. / Koenig, Matthias; Meissner, Joern.

In: International Journal of Production Economics, Vol. 166, 08.2015, p. 11-19.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Koenig, M & Meissner, J 2015, 'Value-at-risk optimal policies for revenue management problems', International Journal of Production Economics, vol. 166, pp. 11-19. https://doi.org/10.1016/j.ijpe.2015.03.027

APA

Koenig, M., & Meissner, J. (2015). Value-at-risk optimal policies for revenue management problems. International Journal of Production Economics, 166, 11-19. https://doi.org/10.1016/j.ijpe.2015.03.027

Vancouver

Koenig M, Meissner J. Value-at-risk optimal policies for revenue management problems. International Journal of Production Economics. 2015 Aug;166:11-19. https://doi.org/10.1016/j.ijpe.2015.03.027

Author

Koenig, Matthias ; Meissner, Joern. / Value-at-risk optimal policies for revenue management problems. In: International Journal of Production Economics. 2015 ; Vol. 166. pp. 11-19.

Bibtex

@article{ce6887da331c40bea235728f2b58c0d1,
title = "Value-at-risk optimal policies for revenue management problems",
abstract = "Abstract Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We introduce a computational method for determining policies which optimises the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. We reduce the state space used in the dynamic programming in order to provide a solution which is feasible and has less computational requirements. Numerical examples and comparison with other risk-sensitive approaches are discussed.",
keywords = "Capacity control, Revenue management, Risk, Value-at-risk",
author = "Matthias Koenig and Joern Meissner",
year = "2015",
month = aug,
doi = "10.1016/j.ijpe.2015.03.027",
language = "English",
volume = "166",
pages = "11--19",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Value-at-risk optimal policies for revenue management problems

AU - Koenig, Matthias

AU - Meissner, Joern

PY - 2015/8

Y1 - 2015/8

N2 - Abstract Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We introduce a computational method for determining policies which optimises the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. We reduce the state space used in the dynamic programming in order to provide a solution which is feasible and has less computational requirements. Numerical examples and comparison with other risk-sensitive approaches are discussed.

AB - Abstract Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We introduce a computational method for determining policies which optimises the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. We reduce the state space used in the dynamic programming in order to provide a solution which is feasible and has less computational requirements. Numerical examples and comparison with other risk-sensitive approaches are discussed.

KW - Capacity control

KW - Revenue management

KW - Risk

KW - Value-at-risk

U2 - 10.1016/j.ijpe.2015.03.027

DO - 10.1016/j.ijpe.2015.03.027

M3 - Journal article

VL - 166

SP - 11

EP - 19

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

ER -